Public health surveillance systems in sub-Saharan Africa face challenges in data quality and predictive utility, limiting proactive resource allocation for disease prevention. A methodological evaluation of such systems is required to enhance their capacity for forecasting and risk reduction. This case study aimed to methodologically evaluate a national surveillance system and develop a robust time-series forecasting model to predict notifiable disease incidence, thereby providing a tool for measuring potential public health risk reduction. We conducted a retrospective analysis of surveillance data, assessing completeness, timeliness, and representativeness. A seasonal autoregressive integrated moving average (SARIMA) model was developed for forecasting, specified as $\phi(B)\Phi(B^s)(1-B)^d(1-B^s)^D y_t = \theta(B)\Theta(B^s)\epsilon_t$, where parameters were estimated using maximum likelihood. Model performance was validated via rolling-origin cross-validation. The methodological evaluation revealed a 22% improvement in data completeness following targeted interventions in sentinel sites. The SARIMA(1,1,1)(0,1,1)_{12} model provided accurate 24-month forecasts, with a 95% prediction interval for annual malaria incidence demonstrating a likely decrease of 8-15% under current intervention scenarios. The integrated methodological and modelling approach proved effective for both evaluating surveillance system performance and generating reliable forecasts, establishing a framework for quantifying the impact of public health interventions. Implement routine forecasting using the validated model to guide district-level resource allocation. Institutionalise continuous methodological audits of surveillance data streams to maintain forecast integrity and system robustness. public health surveillance, time-series analysis, forecasting, SARIMA, Kenya, risk assessment This study provides a novel, integrated framework that links surveillance system evaluation directly to quantitative forecasting, offering a new mechanism for policy-makers to project the risk-reduction impact of health interventions.